Open actionable advisories and recorded vendor fixes. This public package view has no inventory attached and cannot establish whether your deployment is affected. A vendor fix never resolves your finding; verify your installed version and the source guidance.
Open advisories
8 actionable advisories, newest first. Withdrawn advisories and news are excluded. Coverage is partial.
DISPUTED An issue was discovered in NumPy 1.16.2 and earlier. It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources.
Recommended action: Upgrade to 1.16.3 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
init.py in f2py in NumPy before 1.8.1 allows local users to write to arbitrary files via a symlink attack on a temporary file.
Recommended action: Upgrade to 1.8.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
(1) core/tests/testmemmap.py, (2) core/tests/testmultiarray.py, (3) f2py/f2py2e.py, and (4) lib/tests/testio.py in NumPy before 1.8.1 allow local users to write to arbitrary files via a symlink attack on a temporary file.
Recommended action: Upgrade to 1.8.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
The numpy.pad function in Numpy 1.13.1 and older versions is missing input validation. An empty list or ndarray will stick into an infinite loop, which can allow attackers to cause a DoS attack.
Recommended action: Upgrade to 1.13.3 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Null Pointer Dereference vulnerability exists in numpy.sort in NumPy < and 1.19 in the PyArrayDescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays.
Recommended action: Upgrade to 1.19 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Buffer overflow in the arrayfrompyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values.
Recommended action: Upgrade to 1.19 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArrayNewFromDescrint function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. NOTE: The vendor does not agree this is a vulnerability; In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.
Recommended action: Upgrade to 1.21 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Incomplete string comparison in the numpy.core component in NumPy1.9.x, which allows attackers to fail the APIs via constructing specific string objects.
Recommended action: Upgrade to 1.22 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.